Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints, (ii) accurate resource prediction, (iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints, (ii) Kalman-filter estimates for resource prediction, and (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of compute units servicing workloads. The integration of our three proposals into a single CaaS platform is shown to provide for more than 27 reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38 to 60 reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale).
In order to provide for a viable service, a CaaS provider must be able to minimize the monetary cost incurred by the use of cloud CUs and schedule workloads to be executed in the most effective manner. To this end, there have been numerous recent proposals for cloud resource management. Gandhi propose their own version of Autoscale, which terminates servers that have been idle for more than a specified time, while consolidating jobs on less CUs to lower cost @cite_12 . Paya propose a system which expands on this by using multiple sleep states to improve performance @cite_30 . Song propose optimal allocation of CUs according to pricing and demand distributions @cite_5 . Ranjan investigate architectural elements of content-delivery networks with cloud-computing support @cite_26 . Finally, Jung propose using genetic algorithms for multi-user workload scheduling on various CUs @cite_1 .
@cite_30 @cite_26 @cite_1 @cite_5 @cite_12
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In this paper, we introduce an energy-aware operation model used for load balancing and application scaling on a cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature. The growing ubiquity of Internet and cloud computing is having significant impact on media-related industries. These industries are using the Internet and cloud as a medium to enable creation, search, management and consumption of their content. Primarily, Content Delivery Networks (CDNs) are deployed for distributing multimedia content to the end-users. However, existing approaches to architecting CDNs have several limitations. Firstly, they do not harness multiple public cloud services for optimizing cost to performance ratio. Secondly, they lack support for dynamic and personalized content creation and distribution. Finally, they do not support end-to-end content lifecycle operations (production, deployment, consumption, personalization, and distribution). To overcome these limitations, in this paper, we propose, develop and validate a novel system called MediaWise Cloud Content Orchestrator (MCCO). MCCO expands the scope of existing CDNs with novel multi-cloud deployment. It enables content personalization and collaboration capabilities. Further, it facilitates do-it-yourself creation, search, management, and consumption of multimedia content. It inherits the pay-as-you-go models and elasticity that are offered by commercially available cloud services. In this paper, we discuss our vision, the challenges and the research objectives pertaining to MCCO for supporting next generation streamed, interactive, and collaborative high resolution multimedia content. We validated our system thorugh MCCO prototype implementation. Further, we conducted a set of experiments to demonstrate the functionality of MCCO. Finally, we compare the content orchestration features supported by MCCO to existing CDNs against the envisioned objectives of MCCO. Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. Cloud computing based on the spot market helps a user to obtain resources at a lower cost. However, these resources may be unreliable. In this paper, we propose an estimation-based distributed task workflow scheduling scheme that reduces the estimated generation compared to Genetic Algorithm (GA). Moreover, our scheme executes a user’s job within selected instances and stretches the user’s cost. The simulation results, based on a before-and-after estimation comparison, reveal that the task size is determined based on the performance of each instance and the task is distributed among the different instances. Therefore, our proposed estimation-based task load balancing scheduling technique achieves the task load balancing according to the performance of instances. Amazon introduced Spot Instance Market to utilize the idle resources of Amazon Elastic Compute Cloud (EC2) more efficiently. The price of a spot instance changes dynamically according to the current supply and demand for cloud resources. Users can bid for a spot instance and the job request will be granted if the current spot price falls below the bid, whereas the job will be terminated if the spot price exceeds the bid. In this paper, we investigate the problem of designing a bidding strategy from a cloud service broker's perspective, where the cloud service broker accepts job requests from cloud users, and leverages the opportunistic yet less expensive spot instances for computation in order to maximize its own profit. In this context, we propose a profit aware dynamic bidding (PADB) algorithm, which observes the current spot price and selects the bid adaptively to maximize the time average profit of the cloud service broker. We show that our bidding strategy achieves a near-optimal solution, i.e., (1−∈) of the optimal solution to the profit maximization problem, where ∈ can be arbitrarily small. The proposed dynamic bidding algorithm is self-adaptive and requires no a priori statistical knowledge on the distribution of random job sizes from cloud users. Energy costs for data centers continue to rise, already exceeding $15 billion yearly. Sadly much of this power is wasted. Servers are only busy 10--30p of the time on average, but they are often left on, while idle, utilizing 60p or more of peak power when in the idle state. We introduce a dynamic capacity management policy, AutoScale, that greatly reduces the number of servers needed in data centers driven by unpredictable, time-varying load, while meeting response time SLAs. AutoScale scales the data center capacity, adding or removing servers as needed. AutoScale has two key features: (i) it autonomically maintains just the right amount of spare capacity to handle bursts in the request rate; and (ii) it is robust not just to changes in the request rate of real-world traces, but also request size and server efficiency. We evaluate our dynamic capacity management approach via implementation on a 38-server multi-tier data center, serving a web site of the type seen in Facebook or Amazon, with a key-value store workload. We demonstrate that AutoScale vastly improves upon existing dynamic capacity management policies with respect to meeting SLAs and robustness.