Given a large variety of resources and billing contracts offered by today’s cloud providers, customers face a nontrivial optimization challenge for their application workloads. A number of works are dealing with either billing contracts selection optimization or resource types selection. We argue that the largest cost savings to elastic workloads result from jointly optimizing heterogeneous resources and billing contracts selection. To this end, we introduce a novel cloud control and management framework and formulate a novel optimization problem called Heterogeneous Resource Reservation (HRR). We evaluate our solution through a thorough simulation study using publicly available cloud workload data as well as internal anonymous customer data. For these data our approach attain dramatic cost savings compared to the current state of the art.

Hybrid switching combines a high-bandwidth optical circuit switch in parallel with a low-bandwidth electronic packet switch. It presents an appealing solution for scaling datacenter architectures. Unfortunately, it does not fit many traffic patterns produced by typical datacenter applications, and in particular the skewed traffic patterns that involve highly intensive one-to-many and many-to-one communications.

Over the recent years we witness a massive growth of cloud usage, accelerated by new types of 'born-to-the-cloud' workloads. These new types of workloads are increasingly multi-component, dynamic and often present highly intensive communication patterns. Massive innovation of Data Center Network (DCN) technologies is required to support the demand, giving raise to new network topologies, new network control paradigms, and management models. One particularly promising technology candidate for improving the DCN efficiency is Optical Circuit Switching (OCS).

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP) that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSP technique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.

The placement of virtual machines (VMs) on a cluster of hosts under multiple constraints, including administrative (security, regulations) resource-oriented (capacity, energy), and QoS-oriented (performance) is a highly complex task. We define a new high-availability property for a VM; when a VM is marked as k-resilient, as long as there are up to k host failures, it should be guaranteed that it can be relocated to a non-failed host without relocating other VMs. Together with Hardware Predictive Failure Analysis and live migration, which enable VMs to be evacuated from a host before it fails, this property allows the continuous running of YMs on the cluster despite host failures. The complexity of the constraints associated with k-resiliency, which are naturally expressed by Second Order logic statements, prevented their integration into the placement computation until now. We present a novel algorithm which enables this integration by transforming the k-resiliency constraints to rules consumable by a generic Constraint Programming engine, prove that it guarantees the required resiliency and describe the implementation. We provide some preliminary results and compare our high availability support with naive solutions.

Fischer, Lynch and Paterson showed in a fundamental paper that achieving a distributed agreement for N > I processors is impossible in the presence of one faulty processor.This result was later extended by Moran and Wolfstahl who showed that it holds for any task with a connected input graph and a disconnected decision graph (whcrc a vcrtcx in the input [decision] graph is an N-tuple of input [decision] values of the processors, and there is an edge connecting two vertices if and only if they differ in exactly one component),In this paper we extend that latter result, and in fact we set the exact bordedine between solvable and unsolvable tasks, by giving a necessary and sufficient condition for a task to be solvable in the presence of a faulty processor.We present a universal protocol which solves any task which is found to be solvable by our condition.Using our characterization, we derive a novel technique to prove lower bounds on the number of messages that must be sent due to processor failure; specifically, we show that for each fixed JV > 2 there exist distributed tasks for Iv processors that can be solved in the presence of a faulty processor, but any protocol that solves them must send arbitrarily many messages in the worst case.