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Amdahl’s Law
It is typically cheaper to add a new node to a system in order to
achieve improved performance than to perform performance
tuning to improve the capacity that each node can handle. But this
approach can have diminishing returns as indicated by Amdahl’s
Law.
Consider the execution of a given program on a uni- processor
workstation with a total execution time of T minutes. Now, let’s say
that the program has been parallelized or partitioned for parallel
execution on a cluster of many processing nodes.
Assume that a fraction α of the code must be executed sequentially,
called the sequential block. Therefore, (1 - α) of the code can be
compiled for parallel execution by n processors. The total execution
time of program is calculated by:
α T + (1 - α) T / n
Where the first term is the sequential execution time on a single
processor and the second term is the parallel execution time on n
processing nodes.
All system or communication overhead is ignored here. The I/O and
exception handling time is also not included in the speedup analysis.
Amdahl’s Law Amdahl’s Law states that the Speedup Factor of using
the n-processor system over the use of a single processor is expressed
by
Speedup = S = T / [α T + (1 - α) T / n]
= 1 / [α + (1 - α) / n]
The maximum speed up of n is achievable only when α = 0, i.e. the
entire program is parallelizable.
As the cluster becomes sufficiently large, i.e. n ∞, then S 1 / α, an
upper bound on the speedup S. This upper bound is independent of
the cluster size, n. The sequential bottleneck is the portion of the
code that cannot be parallelized.
Example, α = 0.25 and so (1 – 0.25) = 0.75 then the maximum speed
up, S = 4 even if one uses hundreds of processors.](https://image.slidesharecdn.com/cloudcomputingsystemmodelsfordistributedandcloudcomputing-161223052606/85/Cloud-Computing-System-models-for-Distributed-and-cloud-computing-Performance-Security-and-Energy-Efficiency-of-Cloud-Computing-9-320.jpg)
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Amdahl’s Law teaches us that we should make the sequential
bottleneck as small as possible. Increasing the cluster size alone may
not result in a good speed up in this case.
Amdahl’s Law
• Example: suppose 70% of a program can be sped up if
parallelized and run on multiple CPUs instead of one CPU.
• N = 4 processors
S = 1 / [0.3 + (1 – 0.3) / 4] = 2.105
• Doubling the number of processors to N = 8 processors
S = 1 / [0.3 + (1 – 0.3) / 8] = 2.581
Double the processing power has only improved the speedup by
roughly one-fifth. Therefore, throwing in more hardware is not
necessarily the optimal approach.
System Efficiency
To execute a fixed workload on n processors, parallel processing
may lead to a system efficiency defined as:
System Efficiency, E = S / n = 1 / [α n + (1 - α)]
System efficiency can be rather low if the cluster size is very large.
Example: To execute a program on a cluster with n = 4, α = 0.25 and
so
(1 – 0.25) = 0.75,
E = 1 / [0.25 * 4 + 0.75] = 0.57 or 57%
Now if we have 256 nodes (i.e. n = 256)
E = 1 / [0.25 * 256 + 0.75] = 0.015 or 1.5%
This is because only a few processors (4, as in the previous case) are
kept busy, while the majority of the processors (or nodes) are left
idling.](https://image.slidesharecdn.com/cloudcomputingsystemmodelsfordistributedandcloudcomputing-161223052606/85/Cloud-Computing-System-models-for-Distributed-and-cloud-computing-Performance-Security-and-Energy-Efficiency-of-Cloud-Computing-10-320.jpg)
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engage, and oversee the cloud services that can best fulfill those
needs.
The section titled “Cloud Security Assessment” provides customers
with an efficient method of assessing the security capabilities of
cloud providers and assessing their individual risk. A questionnaire for
customers to conduct their own assessment across each of the
critical security domains is provided.
A related document, the Practical Guide to Cloud Service
Agreements [1], provides additional guidance on evaluating security
criteria from prospective cloud providers. The guide titled Cloud
Security Standards: What to Expect & Negotiate [2] highlights the
security standards and certifications that are currently available in
the market as well as the cloud specific security standards that are
currently being developed.](https://image.slidesharecdn.com/cloudcomputingsystemmodelsfordistributedandcloudcomputing-161223052606/85/Cloud-Computing-System-models-for-Distributed-and-cloud-computing-Performance-Security-and-Energy-Efficiency-of-Cloud-Computing-21-320.jpg)
Cloud Computing System models for Distributed and cloud computing & Performance, Security, and Energy Efficiency of Cloud Computing
- 1. 1 System models for Distributed and cloud computing Performance, Security, and Energy Efficiency of Cloud Computing Haris Sarfraz
- 2. 2 Presented to the Faculty of the Department of Computer Sciences Federal Urdu Science Art, Science & Technologies. Gulshan Campus In the fulfillment of the course “Cloud Computing” Semester: 7th BSCS Evening Program Submitted to: Sir. Muhammad Sajid By: Haris Sarfraz Enrollment No: BS/E/20048/13/CS
- 3. 3 Question # 1. Explain System models for Distributed and cloud computing? System Models for Distributed and Cloud Computing • These can be classified into 4 groups: clusters, peer-to-peer networks, grids, and clouds. • A computing cluster consists of interconnected stand-alone computers which work cooperatively as a single integrated computing resource. The network of compute nodes are connected by LAN/SAN and are typically homogeneous with distributed control running Unix/Linux. They are suited to HPC.
- 4. 4 Peer-to-peer (P2P) Networks In a P2P network, every node (peer) acts as both a client and server. Peers act autonomously to join or leave the network. No central coordination or central database is needed. No peer machine has a global view of the entire P2P system. The system is self-organizing with distributed control. Unlike the cluster or grid, a P2P network does not use dedicated interconnection network. P2P Networks are classified into different groups: Distributed File Sharing: content distribution of MP3 music, video, etc. E.g. Gnutella, Napster, BitTorrent. Collaboration P2P networks: Skype chatting, instant messaging, gaming etc. Distributed P2P computing: specific application computing such as SETI@home provides 25 Tflops of distributed computing power over 3 million Internet host machines. Computational and Data Grids Grids are heterogeneous clusters interconnected by high-speed networks. They have centralized control, are server-oriented with authenticated security. They are suited to distribute supercomputing. E.g. TeraGrid. Like an electric utility power grid, a computing grid offers an infrastructure that couples computers, software/middleware, people, and sensors together. The grid is constructed across LANs, WANs, or Internet backbones at a regional, national, or global scale.
- 5. 5 The computers used in a grid include servers, clusters, and supercomputers. PCs, laptops, and mobile devices can be used to access a grid system. Clouds A Cloud is a pool of virtualized computer resources. A cloud can host a variety of different workloads, including batch-style backend jobs and interactive and user-facing applications. Workloads can be deployed and scaled out quickly through rapid provisioning of VMs. Virtualization of server resources has enabled cost effectiveness and allowed cloud systems to leverage low costs to benefit both users and providers. Cloud system should be able to monitor resource usage in real time to enable rebalancing of allocations when needed. Cloud computing applies a virtualized platform with elastic resources on demand by provisioning hardware, software, and data sets dynamically. Desktop computing is moved to a service-oriented platform using server clusters and huge databases at datacenters. Advantage of Clouds over Traditional Distributed Systems Traditional distributed computing systems provided for on-premise computing and were owned and operated by autonomous administrative domains (e.g. a company).
- 6. 6 These traditional systems encountered performance bottlenecks, constant system maintenance, poor server (and other resource) utilization, and increasing costs associated with hardware/software upgrades. Cloud computing as an on-demand computing paradigm resolves or relieves many of these problems. Software Environments for Distributed Systems and Clouds: Service-Oriented Architecture (SOA) Layered Architecture In web services, Java RMI, and CORBA, an entity is, respectively, a service, a Java remote object, And a CORBA object. These build On the TCP/IP network stack. On Top of the network stack we have A base software environment, which Would be .NET/Apache Axis for web Services, the JVM for Java, and the ORB network for CORBA. On top of This base environment, a higher level Environment with features specific to The distributed computing Environment is built. Loose coupling and support of heterogeneous implementations make services more attractive than distributed objects.
- 7. 7 Performance Metrics and Scalability Analysis • Performance Metrics: • CPU speed: MHz or GHz, SPEC benchmarks like SPECINT • Network Bandwidth: Mbps or Gbps • System throughput: MIPS, TFlops (tera floating-point operations per second), TPS (transactions per second), IOPS (IO operations per second) • Other metrics: Response time, network latency, system availability • Scalability: • Scalability is the ability of a system to handle growing amount of work in a capable/efficient manner or its ability to be enlarged to accommodate that growth. • For example, it can refer to the capability of a system to increase total throughput under an increased load when resources (typically hardware) are added. Scalability Scale Vertically To scale vertically (or scale up) means to add resources to a single node in a system, typically involving the addition of CPUs or memory to a single computer. Tradeoffs There are tradeoffs between the two models. Larger numbers of computers means increased management complexity, as well as a more complex programming model and issues such as throughput
- 8. 8 and latency between nodes. Also, some applications do not lend themselves to a distributed computing model. In the past, the price difference between the two models has favored "scale up" computing for those applications that fit its paradigm, but recent advances in virtualization technology have blurred that advantage, since deploying a new virtual system/server over a hypervisor is almost always less expensive than actually buying and installing a real one. Scalability • One form of scalability for parallel and distributed systems is: • Size Scalability This refers to achieving higher performance or more functionality by increasing the machine size. Size in this case refers to adding processors, cache, and memory, storage, or I/O channels. • Scale Horizontally and Vertically Methods of adding more resources for a particular application fall into two broad categories: Scale Horizontally To scale horizontally (or scale out) means to add more nodes to a system, such as adding a new computer to a distributed software application. An example might be scaling out from one Web server system to three. The scale-out model has created an increased demand for shared data storage with very high I/O performance, especially where processing of large amounts of data is required.
- 9. 9 Amdahl’s Law It is typically cheaper to add a new node to a system in order to achieve improved performance than to perform performance tuning to improve the capacity that each node can handle. But this approach can have diminishing returns as indicated by Amdahl’s Law. Consider the execution of a given program on a uni- processor workstation with a total execution time of T minutes. Now, let’s say that the program has been parallelized or partitioned for parallel execution on a cluster of many processing nodes. Assume that a fraction α of the code must be executed sequentially, called the sequential block. Therefore, (1 - α) of the code can be compiled for parallel execution by n processors. The total execution time of program is calculated by: α T + (1 - α) T / n Where the first term is the sequential execution time on a single processor and the second term is the parallel execution time on n processing nodes. All system or communication overhead is ignored here. The I/O and exception handling time is also not included in the speedup analysis. Amdahl’s Law Amdahl’s Law states that the Speedup Factor of using the n-processor system over the use of a single processor is expressed by Speedup = S = T / [α T + (1 - α) T / n] = 1 / [α + (1 - α) / n] The maximum speed up of n is achievable only when α = 0, i.e. the entire program is parallelizable. As the cluster becomes sufficiently large, i.e. n ∞, then S 1 / α, an upper bound on the speedup S. This upper bound is independent of the cluster size, n. The sequential bottleneck is the portion of the code that cannot be parallelized. Example, α = 0.25 and so (1 – 0.25) = 0.75 then the maximum speed up, S = 4 even if one uses hundreds of processors.
- 10. 10 Amdahl’s Law teaches us that we should make the sequential bottleneck as small as possible. Increasing the cluster size alone may not result in a good speed up in this case. Amdahl’s Law • Example: suppose 70% of a program can be sped up if parallelized and run on multiple CPUs instead of one CPU. • N = 4 processors S = 1 / [0.3 + (1 – 0.3) / 4] = 2.105 • Doubling the number of processors to N = 8 processors S = 1 / [0.3 + (1 – 0.3) / 8] = 2.581 Double the processing power has only improved the speedup by roughly one-fifth. Therefore, throwing in more hardware is not necessarily the optimal approach. System Efficiency To execute a fixed workload on n processors, parallel processing may lead to a system efficiency defined as: System Efficiency, E = S / n = 1 / [α n + (1 - α)] System efficiency can be rather low if the cluster size is very large. Example: To execute a program on a cluster with n = 4, α = 0.25 and so (1 – 0.25) = 0.75, E = 1 / [0.25 * 4 + 0.75] = 0.57 or 57% Now if we have 256 nodes (i.e. n = 256) E = 1 / [0.25 * 256 + 0.75] = 0.015 or 1.5% This is because only a few processors (4, as in the previous case) are kept busy, while the majority of the processors (or nodes) are left idling.
- 11. 11 Fault Tolerance and System Availability • High availability (HA) is desired in all clusters, grids, P2P networks, and cloud systems. A system is highly available if it has a long Mean Time to Failure (MTTF) and a short Mean Time to Repair (MTTR). • System Availability = MTTF / (MTTF + MTTR) • All hardware, software, and network components may fail. Single points of failure that bring down the entire system must be avoided when designing distributed systems. • Adding hardware redundancy, increasing component reliability, designing for testability all help to enhance system availability and dependability. • In general, as a distributed system increases in size, availability decreases due to a higher chance of failure and a difficulty in isolating failures.
- 12. 12 Question # 2. Explain the performance, Security, and Energy Efficiency of Cloud Computing? Performance Challenges in Cloud Computing Cloud computing is important for the today’s demanding business requirements. The cloud computing concept, with its salient features, and the three Cloud Service delivery models are explained here. The three cloud delivery models of Software as a Service (SaaS), Platform as a service (PaaS) and Infrastructure as a Service (IaaS) are explored with their Inter-dependencies and performance considerations. Cloud adoption in the business has performance obstacles, and suggestions to overcome these obstacles are provided while suggesting performance considerations for the three cloud delivery models. Performance considerations are vital for the overall success of cloud computing, including the optimum cost of cloud services, reliability and scalability. They require a lot of attention and efforts by the cloud computing providers, integrators and service consumers. WHY CLOUD COMPUTING? It is simply difficult to manage today's complex businesses environments by traditional IT solutions. Some reasons are: • Explosive growth in applications: Web 2.0 social networking, YouTube, Face book, biomedical informatics, space exploration, and business analytics • Extreme scale content generation: e-science and e-business data deluge
- 13. 13 • Extraordinary rate of digital content consumption: digital gluttony: Apple iPhone, iPad, Amazon Kindle • Exponential growth in compute capabilities: multi-core, storage, bandwidth, virtual machines (virtualization) • Very short cycle of obsolescence in technologies: Windows Vista to Windows 7; Java versions; C to C# ; Python • Newer architectures: web services, persistence models, distributed file systems/repositories (Google, Hadoop), multi- core, wireless and mobile. What is cloud computing and why is it distinctive? Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (for example, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. It Involves shifting the bulk of the costs from capital expenditures (CapEx), or buying and installing servers, storage, networking, and related infrastructure to an operating expense (OpEx) model, where you pay for usage of these types of resources. Cloud computing is unique because of its distinct general characteristics: Multi-tenancy: Public cloud service providers often host the cloud services for multiple users within the same infrastructure • Elasticity and scalability: Ability to expand and reduce resources according to your specific service requirement. e.g., you may need a large number of server resources for the duration of a specific task. You can then release these server resources after you complete your task. • Pay-per-use: Pay for cloud services only when you use them, either for the short term (e.g., for CPU time) or for a longer duration (e.g., for cloud-based storage or vault).
- 14. 14 • On demand: One can invoke cloud services on need basis, need not to be part of IT infrastructure—a significant advantage for cloud use as opposed to internal IT services. • Resiliency: Cloud can completely isolate the failure of server and storage resources from cloud users. (Work can be migrated to a different physical resource in the cloud with or without user awareness and intervention.) • Workload movement: It is important for resiliency and cost considerations, service providers can migrate workloads across servers — both inside the data center and across data centers (even in a different geographic area). Typical reasons for workload movement are due to a catastrophic event in a geographic region (say Hurricane Sandy in the US). Then the corkload can be moved to some other geographic region for the time being, or there can be some other business drivers for the workload movement to get these advantages: • Less cost - It is less expensive to run a workload in a data center in another area based on time of day or power requirements. • Efficient –Better resources / network bandwidth availability. For example, US nightly processing in India day time is less costly and more efficient. • Regulatory considerations - For certain types of workloads, e.g. New York stock exchange processing from India. CLOUD SERVICE DELIVERY MODELS AND THEIR PERFORMANCE CHALLENGES Cloud service delivery models Software as a Service (SaaS) Enterprises will have software licenses to support the various applications used in their daily business. These applications could be in human resources, finance, or customer relationship management.
- 15. 15 The traditional option is to obtain the desktop and server licenses for the software products used. Software as a Service (SaaS) allows the enterprise to obtain the same functions through a hosted service from a provider through a network connection. Consumer services include social platforms (e.g. Face book) or online email services (e.g. Gmail). There are also increasing numbers of business services being delivered as-a-service (e.g. software package rendering through VDO / Citrix server to the mass public). Centralized services typically designed to cater for large numbers of end users over Internet. SaaS reduces the complexity of software installation, maintenance, upgrades, and patches for the IT team within the enterprise, because the software is now managed centrally at the SaaS provider’s facilities. SaaS providers are responsible to monitor the application-delivery performance. Platform as a Service (PaaS) Unlike the fixed application functionality offered by SaaS, Platform as a Service (PaaS) provides a software platform on which users can build their own applications and host them on the PaaS provider’s infrastructure (e.g. Google with its App-Engine or Force.com APIs). • The software platform is used as a development framework to provide services for use by applications. • PaaS is a true cloud model in that applications do not need to worry about the scalability of the underlying hardware and software platform. • PaaS providers are responsible to monitor the application delivery performance elasticity and scalability.
- 16. 16 Infrastructure as a Service (IaaS) An Infrastructure as a Service (IaaS) provider offers you raw computing, storage, and network infrastructure so that you can load your own software, including operating systems and applications, on to this infrastructure (e.g. Amazon’s Elastic Computing Cloud (EC2) service). • This scenario is equivalent to a hosting provider provisioning physical servers and storage, and letting you install your own OS, web services, and database applications over the provisioned machines. • Greatest degree of control of the three models, resource requirement management, is required to exploit IaaS well. • Scaling and elasticity are user’s responsibility and not the provider’s responsibility. Inter-dependencies of delivery models and their performance measures The cloud can also be defined as the virtualized infrastructure found on the lowest level of the solution stack (i.e. IaaS layer as in Figure 1). The higher service layers depend on the underlying supporting service layers. Service providers can be service users as well: • A SaaS provider may be a SaaS user • A SaaS provider may or may not be a PaaS user • SaaS and PaaS providers are directly or indirectly IaaS users Summary - Characteristics of Performance Measures • SaaS performance measures are directly perceived by users as business transaction response times and throughput, technical service reliability and availability, and by scalability of the applications.
- 17. 17 • PaaS performance measures are indirectly perceived by users and defined as technical transaction response times and throughput, technical service reliability and availability, and by IaaS Performance Measures are defined as infrastructure performance, capacity, reliability, availability, and scalability. • In general, characteristics of performance measures of the upper service layers depend on those characteristics in the underlying Layers, e.g. SaaS layer scalability depends on IaaS layer scalability. PERFORMANCE ASPECTS ARE MAJOR OBSTACLES IN CLOUD ADOPTION AND GROWTH The success of Cloud deployments is highly dependent on practicing holistic performance engineering and capacity management techniques. A majority of the obstacles for adoption and growth of cloud computing are related to the basic performance aspects, such as availability, performance, capacity, or scalability. Please refer below to Table 1 for the obstacles and opportunities details. • Potential cloud solutions to overcome these obstacles need to be carefully assessed for their authenticity in real-life situations. • Performance engineers need to get to the bottom of the technical transactions of underlying cloud services before advising cloud computing users and cloud computing providers for the cloud services. • The degree to which cloud services can meet agreed service- level requirements for availability, performance, and scalability Can be estimated by using performance modeling techniques, so that potential performance anti-patterns can be detected before they happen. • In the absence of sophisticated tooling for automated monitoring, the automatic provisioning and usage-based costing (metering) facilities rely mainly on fine-grained capacity management. Until more data collection, analysis, and forecasting are in place, capacity management is more opportune than ever.
- 18. 18 • Irrespective of sophisticated tooling for automated monitoring, cloud computing users need to analyze their demand for capacity and their requirements for performance. In their contract with cloud computing providers, users should always take a bottom-line approach to accurately formulate their service-level requirements.
- 19. 19 Advanced Data Analytics for better performance An individual enterprise may produce terabytes of log data per month which can contain millions of events per second. The techniques for gathering monitoring data have become a lot better through the development of performance tools for in-house enterprise systems; however the analysis of the large volume of data collected has been still a major challenge. • There is a burning need of having efficient and preferred log analytics systems in cloud environment, with the surfacing of new cloud technologies on these challenges such as log management as-a-service. • A log management as-a-service technology handling log analysis for large numbers of enterprises must be able to manage millions of events per second, performing visualization, analysis and alerting in real time to allow for autonomic management of the system. • Cloud has produced new challenges due to the larger scale of systems and the much larger volumes of data produced by these systems. Real time analytics is a growing area and provides challenges in the analysis of upwards of millions of events per second with real time constraints. • Real time analytics can be a BIG aid for performance monitoring; this is another emerging rich area of research. Real time analytics with time constraints will certainly enhance the performance management of cloud based systems.
- 20. 20 Cloud computing security Introduction Failure to ensure appropriate security protection when using cloud services could ultimately result in higher costs and potential loss of business, thus eliminating any of the potential benefits of cloud computing. The aim of this guide is to provide a practical reference to help enterprise information technology (IT) and business decision makers analyze the security implications of cloud computing on their business. The paper includes a list of steps, along with guidance and strategies, designed to help these decision makers evaluate and compare security offerings from different cloud providers in key areas. When considering a move to cloud computing, customers must have a clear understanding of potential security benefits and risks associated with cloud computing, and set realistic expectations with their cloud provider. Consideration must be given to the different service categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) as each model brings different security requirements and responsibilities. Additionally, this paper highlights the role that standards play to improve cloud security and also identifies areas where future standardization could be effective. The section titled “Current Cloud Security Landscape” provides an overview of the security and privacy challenges pertinent to cloud computing and points out considerations that organizations should weigh when migrating data, applications, and infrastructure to a cloud computing environment. The section titled “Cloud Security Guidance” is the heart of the guide and includes the steps that can be used as a basis for evaluation of cloud provider security. It discusses the threats, technology risks, and safeguards for cloud computing environments, and provides the insight needed to make informed IT decisions on their treatment. Although guidance is provided, each organization must perform its own analysis of its needs, and assess, select,
- 21. 21 engage, and oversee the cloud services that can best fulfill those needs. The section titled “Cloud Security Assessment” provides customers with an efficient method of assessing the security capabilities of cloud providers and assessing their individual risk. A questionnaire for customers to conduct their own assessment across each of the critical security domains is provided. A related document, the Practical Guide to Cloud Service Agreements [1], provides additional guidance on evaluating security criteria from prospective cloud providers. The guide titled Cloud Security Standards: What to Expect & Negotiate [2] highlights the security standards and certifications that are currently available in the market as well as the cloud specific security standards that are currently being developed.
- 22. 22 Cloud computing security Cloud computing security is the set of control-based technologies and policies designed to adhere to regulatory compliance rules and protect information, data applications and infrastructure associated with cloud computing use. Cloud computing security is the set of control-based technologies and policies designed to adhere to regulatory compliance rules and protect information, data applications and infrastructure associated with cloud computing use. IT operations are sometimes unexpectedly affected by major audit regulations – is your IT team prepared? Explore the critical role your IT team plays in ensuring compliance and review the penalties for non- compliance by downloading this FREE e-guide, which covers any questions you might have regarding 4 major legislative regulations. Because of the cloud's very nature as a shared resource, identity management, privacy and access control are of particular concern. With more organizations using cloud computing and associated cloud providers for data operations, proper security in these and other potentially vulnerable areas have become a priority for organizations contracting with a cloud computing provider. Cloud computing security processes should address the security controls the cloud provider will incorporate to maintain the customer's data security, privacy and compliance with necessary regulations. The processes will also likely include a business continuity and data backup plan in the case of a cloud security breach. Definition - What does Cloud Computing Security mean? Cloud computing security refers to the set of procedures, processes and standards designed to provide information security assurance in a cloud computing environment.
- 23. 23 Cloud computing security addresses both physical and logical security issues across all the different service models of software, platform and infrastructure. It also addresses how these services are delivered (public, private or hybrid delivery model). Techopedia explains Cloud Computing Security Cloud security encompasses a broad range of security constraints from an end-user and cloud provider's perspective, where the end- user will primarily will be concerned with the provider's security policy, how and where their data is stored and who has access to that data. For a cloud provider, on the other hand, cloud computer security issues can range from the physical security of the infrastructure and the access control mechanism of cloud assets, to the execution and maintenance of security policy. Cloud security is important because it is probably the biggest reason why organizations fear the cloud. The Cloud Security Alliance (CSA), a nonprofit organization of industry specialists, has developed a pool of guidelines and frameworks for implementing and enforcing security within a cloud operating environment. Cloud Security Landscape While security and privacy concerns1 are similar across cloud services and traditional non-cloud services, those concerns are amplified by the existence of external control over organizational assets and the potential for mismanagement of those assets. Transitioning to public cloud computing involves a transfer of responsibility and control to the cloud provider over information as well as system components that were previously under the customer’s direct control. Despite this inherent loss of control, the cloud service customer still needs to take responsibility for its use of cloud computing services in
- 24. 24 order to maintain situational awareness, weigh alternatives, set priorities, and effect changes in security and privacy that are in the best interest of the organization. The customer achieves this by ensuring that the contract with the provider and its associated cloud service agreement has appropriate provisions for security and privacy. In particular, the agreement must help maintain legal protections for the privacy of data stored and processed on the provider's systems. The customer must also ensure appropriate integration of cloud computing services with their own systems for managing security and privacy. There are a number of security risks associated with cloud computing that must be adequately addressed ● Loss of governance. In a public cloud deployment, customers cede control to the cloud provider over a number of issues that may affect security. Yet cloud service agreements may not offer a commitment to resolve such issues on the part of the cloud provider, thus leaving gaps in security defenses. ● Responsibility ambiguity. Responsibility over aspects of security may be split between the provider and the customer, with the potential for vital parts of the defenses to be left unguarded if there is a failure to allocate responsibility clearly. This split is likely to vary depending on the cloud computing model used (e.g., IaaS vs. SaaS). ● Authentication and Authorization. The fact that sensitive cloud resources are accessed from anywhere on the Internet heightens the need to establish with certainty the identity of a user -- especially if users now include employees, contractors, partners and customers. Strong authentication and authorization becomes a critical concern. ● Isolation failure. Multi-tenancy and shared resources are defining characteristics of public cloud computing. This risk category covers the failure of mechanisms separating the usage of storage, memory, routing and even reputation between tenants (e.g. so-called guest- hopping attacks). ● Compliance and legal risks. The cloud customer’s investment in achieving certification (e.g., to demonstrate compliance with industry standards or regulatory requirements) may be lost if the
- 25. 25 cloud provider cannot provide evidence of their own compliance with the relevant requirements, or does not permit audits by the cloud customer. The customer must check that the cloud provider has appropriate certifications in place. • Handling of security incidents. The detection, reporting and subsequent management of security breaches may be delegated to the cloud provider, but these incidents impact the customer. Notification rules need to be negotiated in the cloud service agreement so that customers are not caught unaware or informed with an unacceptable delay. ● Management interface vulnerability. Interfaces to manage public cloud resources (such as self-provisioning) are usually accessible through the Internet. Since they allow access to larger sets of resources than traditional hosting providers, they pose an increased risk, especially when combined with remote access and web browser vulnerabilities. ● Application Protection. Traditionally, applications have been protected with defense-in-depth security solutions based on a clear demarcation of physical and virtual resources, and on trusted zones. With the delegation of infrastructure security responsibility to the cloud provider, organizations need to rethink perimeter security at the network level, applying more controls at the user, application and data level. The same level of user access control and protection must be applied to workloads deployed in cloud services as to those running in traditional data centers. This requires creating and managing workload-centric policies as well as implementing centralized management across distributed workload instances. ● Data protection. Here, the major concerns are exposure or release of sensitive data as well as the loss or unavailability of data. It may be difficult for the cloud service customer (in the role of data controller) to effectively check the data handling practices of the cloud provider. This problem is exacerbated in cases of multiple transfers of data, (e.g., between federated cloud services or where a cloud provider uses subcontractors).
- 26. 26 ● Malicious behavior of insiders. Damage caused by the malicious actions of people working within an organization can be substantial, given the access and authorizations they enjoy. This is compounded in the cloud computing environment since such activity might occur within either or both the customer organization and the provider organization. ● Business failure of the provider. Such failures could render data and applications essential to the customer's business unavailable over an extended period. ● Service unavailability. This could be caused by hardware, software or communication network failures. ● Vendor lock-in. Dependency on proprietary services of a particular cloud service provider could lead to the customer being tied to that provider. The lack of portability of applications and data across providers poses a risk of data and service unavailability in case of a change in providers; therefore it is an important if sometimes overlooked aspect of security. Lack of interoperability of interfaces associated with cloud services similarly ties the customer to a particular provider and can make it difficult to switch to another provider. ● Insecure or incomplete data deletion. The termination of a contract with a provider may not result in deletion of the customer’s data. Backup copies of data usually exist, and may be mixed on the same media with other customers’ data, making it impossible to selectively erase. The very advantage of multi-tenancy (the sharing of hardware resources) thus represents a higher risk to the customer than dedicated hardware. ● Visibility and Audit. Some enterprise users are creating a “shadow IT” by procuring cloud services to build IT solutions without explicit organizational approval. Key challenges for the security team are to know about all uses of cloud services within the organization (what resources are being used, for what purpose, to what extent, and by whom), understand what Laws, regulations and policies may apply to such uses, and regularly assess the security aspects of such uses.
- 27. 27 Cloud computing does not only create new security risks: it also provides opportunities to provision improved security services that are better than those many organizations implement on their own. Cloud service providers could offer advanced security and privacy facilities that leverage their scale and their skills at automating infrastructure management tasks. This is potentially a boon to customers who have little skilled security personnel. Cloud Security Guidance As customers transition their applications and data to the cloud, it is critical for them to maintain, or preferably surpass, the level of security they had in their traditional IT environment. This section provides a prescriptive series of steps for cloud customers to evaluate and manage the security of their use of cloud services, with the goal of mitigating risk and delivering an appropriate level of support. The following steps will be discussed in detail below: 1. Ensure effective governance, risk and compliance processes exist 2. Audit operational and business processes 3. Manage people, roles and identities 4. Ensure proper protection of data and information 5. Enforce privacy policies 6. Assess the security provisions for cloud applications 7. Ensure cloud networks and connections are secure 8. Evaluate security controls on physical infrastructure and facilities 9. Manage security terms in the cloud service agreement 10. Understand the security requirements of the exit process Requirements and best practices are highlighted for each step. In addition, each step takes into account the realities of today’s cloud computing landscape and postulates how this space is likely to evolve in the future, including the important role that standards will play to improve interoperability and portability across providers.
- 28. 28 Distinctions between Security and Privacy
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- 30. 30 Energy Efficiency of Cloud Computing Abstract: Cloud computing is an emerging technology which provides metering based services to consumers. Cloud computing offers ITC based services and provide computing resources through virtualization over internet. Data center is heart of cloud computing which contains collection of servers on which Business information is stored and applications run. Data center (includes servers, network, cables, air conditioner etc.) consumes more power and releases huge amount of Carbon-di-oxide (CO2) to the environment. One of the most important challenge in cloud computing is optimization of energy utilization and hence have a green cloud computing. There are many techniques and algorithms used to minimize the energy consumption in cloud. Techniques include DVFS, VM Migration and VM Consolidation. Algorithms are Maximum Bin Packing, Power Expand Min-Max and Minimization Migrations, Highest Potential growth, Random Choice. The main goal of all these approaches is to optimize the energy utilization in cloud. This paper provides overview of literature survey on approaches to have energy efficient cloud. Energy Efficiency of Cloud Computing Most agree that cloud computing is inherently more efficient that on premise computing in each of several dimensions. Last November, I went after two of the easiest to argue gains: utilization and the ability to sell excess capacity (Datacenter Renewable Power Done Right): Cloud computing is a fundamentally more efficiently way to operate compute infrastructure. The increases in efficiency driven by the cloud are many but a strong primary driver is increased utilization. All companies have to provision their compute infrastructure for peak usage. But, they only monetize the actual usage which goes up and down over time. What this leads to incredibly low average utilization levels with 30% being extraordinarily high and 10 to 20% the norm.
- 31. 31 Cloud computing gets an easy win on this dimension. When non- correlated workloads from a diverse set of customers are hosted in the cloud, the peak to average flattens dramatically. Immediately effective utilization sky rockets. Where 70 to 80% of the resources are usually wasted, this number climbs rapidly with scale in cloud computing services flattening the peak to average ratio. To further increase the efficiency of the cloud, Amazon Web Services added an interesting innovation where they sell the remaining capacity not fully consumed by this natural flattening of the peak to average. These troughs are sold on a spot market and customers are often able to buy computing at less the amortized cost of the equipment they are using (Amazon EC2 Spot Instances). Customers get clear benefit. And, it turns out; it’s profitable to sell unused capacity at any price over the marginal cost of power. This means the provider gets clear benefit as well. And, with higher utilization, the environment gets clear benefit as well. Back in June, Lawrence Berkeley National Labs released a study that went after the same question quantitatively and across a much broader set of dimensions. I first came across the report via coverage in Network Computing: Cloud Data Centers: Power Savings or Power drain? The paper was funded by Google which admittedly has an interest in cloud computing and high scale computing in general. But, even understanding that possible bias or influence, the paper is of interest. From Google’s summary of the findings (How Green is the Internet?): Funded by Google, Lawrence Berkeley National Laboratory investigated the energy impact of cloud computing. Their research indicates that moving all office workers in the United States to the cloud could reduce the energy used by information technology by up to 87%. These energy savings are mainly driven by increased data center efficiency when using cloud services (email, calendars, and more). The cloud supports many products at a time, so it can more efficiently distribute resources among many users. That means we can do more with less energy.
- 32. 32 How energy-efficient is cloud computing? Researchers have found that, at high usage levels, the energy required to transport data in cloud computing can be larger than the amount of energy required storing the data conventionally, data storage and data processing are done at the user's own computer, using that computer's storage system and processor. An alternative to this method is cloud computing, which is Internet-based computing that enables users at home or office computers to transfer data to a remote data center for storage and processing. Cloud computing offers potential benefits – especially financial ones – to users, but in a new study, researchers have investigated a different aspect of cloud computing: how does its energy consumption compare with conventional computing? “Many industry participants see the evolution toward mobility will intrinsically mean an evolution toward cloud-based services,” Tucker said. “The reason is that mobile access devices will have limited processing and storage capacity (due to size and power constraints) and so the most convenient place to put the applications and data is in the cloud. The user device will contain little more than a browser when it is started up. Any application or data that it requires will be brought down from the cloud. When that application is finished, its data will be put back into the cloud and the application will be removed from the user device until it is again required. In this way, the user device is kept simple, energy-efficient and cheap.”