2/6/2018 Day 7 Economic-based Cloud Resource Provisioning Introduction • The performance of a distributed system often depends on the maximum load of any of the machines. • Modern datacenters employ server virtualization and consolidation to reduce the cost of operation and to maximize profit. • From time to time, cloud service providers find themselves in a situation where performance becomes an issue. Introduction • We discuss two possible ways to address this situation – Reassignment approach • The higher the maximum load, the longer the execution time of the entire system. • A good load balancing schemes are crucial for efficient computations on distributed systems. – Leasing resources from alternative datacenters owned by federated providers. 1
2/6/2018 Cloud Computing • Cloud computing service providers offer computing resources as utility • Resources are charged by its type and duration Cloud service request provider Cloud consumer J1 J1 Jn • The cost is computed as the fixed charge multiplied by the number of resource instances or service units requested by the consumer. Cloud Service Users • Cloud users pay providers for cloud resource usage. • For example, let ℛ = 𝑠 � , ⋯ , 𝑠 � be the resources used by Cloud users. 𝑉 � = � 𝑑 � � ∙ 𝑢 � �∈ℛ – 𝑑 � denotes the cost of resource 𝑠 � per unit time and 𝑢 � denotes the time for which resource 𝑠 � is utilized. • For example, if the cost of using resource r1 is 2000 per time unit and r2 is 3000 per time unit, where r1 is used for 22 time unit and r2 is used 19 time unit • 𝑉 � = 2000 ∙ 22 + 3000 ∙ 19 = 101,000 Cloud Service Users • Cloud users expect Quality-of-service guarantees from the cloud service providers. • QoS parameters indicate the ability of a service to meet certain requirements for different aspects of the service • For example – Deadline constraint: This represents the time till which the task or the batch of tasks should be finished. – Budget constraint: This represents the restriction on the total cost of executing all tasks. – Cost: resources are provisioned in a cost-efficient way 2
2/6/2018 Cloud User Objectives • User objective can be stated as maximizing QoS such as minimize the usage cost while satisfying the performance requirement of the applications • For example, the objective can be to determine a configuration where all tasks of a user are executed at a minimal cost. Minimize ∑ 𝑑 � � ∙ 𝑢 � �∈ℛ • QoS requirements are commonly formalized in the form of SLAs Cloud Service Provider • Cloud providers need to efficiently manage resources to achieve the performance of their applications and improve the utilization of reserved resources, thereby minimizing the usage cost. • Some objectives – Revenue maximization is one of the objectives of cloud computing service provider. – Resource utilization maximization is another objectives. – This must be done in measured manner is it can lead to system overload. • Research question 1 : An approach that predicts future resource requirements for workload. These predictive approaches can lead to better resource efficiency. Cloud Service Provider • Cloud providers must achieve certain level of QoS • For instance – For budget constraint jobs the cost of performing these jobs cannot exceed a certain budget constraint • Therefore, one of the challenges facing service provider is how to complete jobs with unpredictable submission time under budget and deadline constrains • Research problem : Most of existing work do not consider unpredictable submission time of jobs, as well as budget and deadline constrains simultaneously. 3
2/6/2018 Service Level Agreements • Service Level Agreements (SLAs) form an important component of the contractual relationship between a cloud customer and a cloud service provider Cloud service request provider Cloud consumer J1 J1 Jn • Research question 2 : different cloud services and deployment models will require different approaches to SLAs. • Research question 3 : The global nature of Cloud computing renders SLAs to cross several jurisdictions. Frameworks for handling data privacy is an important addition to cloud computing. Jemal H. Abawajy, Mohd Farhan Md Fudzee, Mohammad Mehedi Hassan, Majed A. AlRubaian: Service level agreement management framework for utility-oriented computing platforms. The Journal of Supercomputing 71(11): 4287-4303 (2015) Penalties for Missing SLA • Total economical penalties for SLA violations the sum of the total proportional penalties costs for unsatisfied demand of resources. • Total economical penalties at instant 𝑢 ( 𝑄 � ); � � 𝑄 � = � � 𝑆 �� ∙ ∆ �� ∙ 𝜚 � ��� ��� – 𝑠 : Number of resources assigned to the Cloud user. For example, if the user is assigned Disk, CPU, RAM memory and network capacity, then 𝑠 = 4 . – 𝑆 �� : Revenue for completing the assigned virtual machine; – 𝜚 � : Penalty factor for resource k, where 𝜚 � ≥ 1 ; – 𝑛 ∶ Number of VMs Cloud Resource Provisioning algorithms • A variety of cloud resource provisioning has been developed – Budget aware Provisioning algorithms • The objective is to complete jobs such that the cost of performing these jobs cannot exceed a certain budget constraint – Cost-aware Provisioning algorithms • The objective is to satisfy the SLA requirements of the user while minimizing the total cost. 4
2/6/2018 Cloud Resource Provisioning algorithms • SLA-aware Provisioning - The objective is to satisfy the SLA requirements of the user while minimizing the total cost. • In fact, the SLA requirements consist of Budget and Deadline. – As a result, SLA-aware algorithms’ objective can be stated as follows: Minimize 𝑈𝐷 = � 𝑑 � � ∙ 𝑢 � �∈ℛ • Subject to • 𝑈𝑝𝑢𝑏𝑚 𝐷𝑝𝑡𝑢 ≤ 𝑐𝑣𝑒𝑓𝑢 • 𝑓𝑦𝑓𝑑𝑣𝑢𝑗𝑝𝑜 𝑢𝑗𝑛𝑓 ≤ 𝑒𝑓𝑏𝑒𝑚𝑗𝑜𝑓 VM allocation optimization • Cloud service providers dynamically receive – requests for the placement of cloud services – The requests have different characteristics according to different dynamic parameters. • It is well known that online decisions made along the operation of a dynamic cloud computing infrastructure needs to be dynamically optimized to avoid negative affects • A common approach for optimization of the current VM allocation is through adjusting resource allocation according to demand in order to satisfy SLA. • It is worth noting that this problem is a multi-objective optimization problem including guaranteeing service quality and maximizing resource utilization with multiple resource constraints. Dynamic VM Placement Rebalancing • The main idea is to proactively monitor the load of the physical machines and dynamically rebalance the load. – Some VMs were migrated from the overloaded physical machine to achieve the goals of performing load balancing, improving QoS and degrading the risk of overloading the CPU resource. – The migrated VMs were redeployed in the normal PMs. • This requires a number of algorithms including identifying overload host detection 5
2/6/2018 Dynamic VM Placement Rebalancing • There are several key problems which should be dealt with: – when a host is supposed to be heavily loaded, where some VMs from the host should be migrated to another host; – when a host is believed to be moderately loaded or lightly loaded, resulting in a decision to keep all VMs on this host unchanged; – when a host is believed to be little-loaded, where all VMs on the host must be migrated to another host; – selecting a VM or more VMs that should be migrated from the heavily loaded; finding a new host to accommodate VMs migrated from heavily loaded or little-loaded hosts. Overload host detection • Overbooked resources may lead to Quality of Service (QoS) degradation, and consequently Service Level Agreement (SLA) violations with economical penalties. • This economical penalties should be minimized for an economical revenue maximization Threshold-based Heuristics • A variety of threshold-based heuristics have been advanced to handle – Single Threshold (ST) [Buyya, et. al] – Double Thresholds (DT) algorithm [Beloglazov and Buyya] – Three Thresholds algorithm [Abawajy, et al] 6
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