An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks

Network Functions Virtualization (NFV) in Software Defined Networks (SDN) emerged as a new technology for creating virtual instances for smooth execution of multiple applications. Their amalgamation provides flexible and programmable platforms to utilize the network resources for providing Quality of Service (QoS) to various applications. In SDN-enabled NFV setups, the underlying network services can be viewed as a series of virtual network functions (VNFs) and their optimal deployment on physical/virtual nodes is considered a challenging task to perform. However, SDNs have evolved from single-domain to multi-domain setups in the recent era. Thus, the complexity of the underlying VNF deployment problem in multi-domain setups has increased manifold. Moreover, the energy utilization aspect is relatively unexplored with respect to an optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the VNF deployment problem in multi-domain SDN setup has been addressed with a primary emphasis on reducing the overall energy consumption for deploying the maximum number of VNFs with guaranteed QoS. The problem in hand is initially formulated as a "Multi-objective Optimization Problem" based on Integer Linear Programming (ILP) to obtain an optimal solution. However, the formulated ILP becomes complex to solve with an increasing number of decision variables and constraints with an increase in the size of the network. Thus, we leverage the benefits of the popular evolutionary optimization algorithms to solve the problem under consideration. In order to deduce the most appropriate evolutionary optimization algorithm to solve the considered problem, it is subjected to different variants of evolutionary algorithms on the widely used MOEA framework (an open source java framework based on multi-objective evolutionary algorithms).Comment: Accepted for publication in IEEE INFOCOM 2019 Workshop on Intelligent Cloud Computing and Networking (ICCN 2019

Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)

Database management systems (DBMSs) carefully optimize complex multi-join queries to avoid expensive disk I/O. As servers today feature tens or hundreds of gigabytes of RAM, a significant fraction of many analytic databases becomes memory-resident. Even after careful tuning for an in-memory environment, a linear disk I/O model such as the one implemented in PostgreSQL may make query response time predictions that are up to 2X slower than the optimal multi-join query plan over memory-resident data. This paper introduces a memory I/O cost model to identify good evaluation strategies for complex query plans with multiple hash-based equi-joins over memory-resident data. The proposed cost model is carefully validated for accuracy using three different systems, including an Amazon EC2 instance, to control for hardware-specific differences. Prior work in parallel query evaluation has advocated right-deep and bushy trees for multi-join queries due to their greater parallelization and pipelining potential. A surprising finding is that the conventional wisdom from shared-nothing disk-based systems does not directly apply to the modern shared-everything memory hierarchy. As corroborated by our model, the performance gap between the optimal left-deep and right-deep query plan can grow to about 10X as the number of joins in the query increases.Comment: 15 pages, 8 figures, extended version of the paper to appear in SoCC'1

Synapse: Synthetic Application Profiler and Emulator

We introduce Synapse motivated by the needs to estimate and emulate workload execution characteristics on high-performance and distributed heterogeneous resources. Synapse has a platform independent application profiler, and the ability to emulate profiled workloads on a variety of heterogeneous resources. Synapse is used as a proxy application (or "representative application") for real workloads, with the added advantage that it can be tuned at arbitrary levels of granularity in ways that are simply not possible using real applications. Experiments show that automated profiling using Synapse represents application characteristics with high fidelity. Emulation using Synapse can reproduce the application behavior in the original runtime environment, as well as reproducing properties when used in a different run-time environments