* Updated the memory usage of Tasks. Still in Progress. * Merged Task and ServiceTask -> Currently not fully working!!! * Fixed bugs that made the merger between Task and ServiceTask not work well. * Updated jdk version for Dockerfile * Removed ServiceFlavor.java and Task.kt
Updated FilterScheduler.kt for performance using a constantly sorted Array
* Updated FilterScheduler for performance
* Updated output format to reduce size * using sum of gpu capacities instead of single max * passing provisioned GPU cores to host view * fix supply update trigger * fixing floating point error, leading to negative demand * fixing double mismatch, due to floating point in precision * adding additional check if demand can be satisfied in the simple way * adds workload invalidation if remaining duration for all resources is 0 * invalidating flow distributors after demand update * spotless apply * updating tests * exporting power consumption of compute resources directly from gpu instead of PSU * using big decimal to avoid floating point in-precision * rolls back to pass-through version of PSU, before GPU implementation * places flowdistributor between PSU and compute resources * adds check to avoid null exception if supply is pushed without demand * fixing task id type * Adds memorizing GPU scheduler * adds boundary for negative remaining work * implemented tests for GPU scheduler filter * Revert "Updated output format to reduce size" This reverts commit 7171de8e0512a863df4962f64560ac7bad1fb48d. * spotless aply --------- Co-authored-by: DanteNiewenhuis <d.niewenhuis@hotmail.com>
* renamed performance counter to distinguish different resource types * added GPU, modelled similar to CPU * added GPUs to machine model * list of GPUs instead of single instance * renamed memory speed to bandwidth * enabled parsing of GPU resources * split powermodel into cpu and GPU powermodel * added gpu parsing tests * added idea of host level scheduling * added tests for multi gpu parsing * renamed powermodel to cpupowermodel * clarified naming of cpu and gpu components * added resource type to flow suplier and edge * added resourcetype * added GPU components and resource type to fragments * added GPU to workload and updated resource usage retrieval * implemented first version of multi resource * added name to workload * renamed perfomance counters * removed commented out code * removed deprecated comments * included demand and supply into calculations * resolving rebase mismatches * moved resource type from flowedge class to common package * added available resources to machinees * cleaner separation if workload is started of simmachine or vm * Replaced exception with dedicated enum * Only looping over resources that are actually used * using hashmaps to handle resourcetype instead of arrays for readability * fixed condition * tracking finished workloads per resource type * removed resource type from flowedge * made supply and demand distribution resource specific * added power model for GPU * removed unused test setup * removed depracated comments * removed unused parameter * added ID for GPU * added GPUs and GPU performance counters (naively) * implemented capturing of GPU statistics * added reminders for future implementations * renamed properties for better identification * added capturing GPU statistics * implemented first tests for GPUs * unified access to performance counters * added interface for general compute resource handling * implemented multi resource support in simmachine * added individual edge to VM per resource * extended compute resource interface * implemented multi-resource support in PSU * implemented generic retrieval of computeresources * implemented mult-resource suppport in vm * made method use more resource specific * implemented simple GPU tests * rolled back frquency and demand use * made naming independent of used resource * using workloads resources instead of VMs to determine available resource * implemented determination of used resources in workload * removed logging statements * implemented reading from workload * fixed naming for host-level allocation * fixed next deadline calculation * fixed forwarding supply * reduced memory footprint * made GPU powermodel nullable * maded Gpu powermodel configurable in topology * implemented tests for basic gpu scheduler * added gpu properties * implemented weights, filter and simple cpu-gpu scheduler * spotless apply * spotless apply pt. 2 * fixed capitalization * spotless kotlin run * implemented coloumn export * todo update * removed code comments * Merged PerformanceCounter classes into one & removed interface * removed GPU specific powermodel * Rebase master: kept both versions of TopologyFactories * renamed CpuPowermodel to resource independent Powermodel Moved it from Cpu package to power package * implementated default of getResourceType & removed overrides if possible * split getResourceType into Consumer and Supplier * added power as resource type * reduced supply demand from arrayList to single value * combining GPUs into one large GPU, until full multi-gpu support * merged distribution policy enum with corresponding factory * added comment * post-rebase fixes * aligned naming * Added GPU metrics to task output * Updates power resource type to uppercase. Standardizes the `ResourceType.Power` enum to `ResourceType.POWER` for consistency with other resource types and improved readability. * Removes deprecated test assertions Removes commented-out assertions in GPU tests. These assertions are no longer needed and clutter the test code. * Renames MaxMinFairnessStrategy to Policy Renames MaxMinFairnessStrategy to MaxMinFairnessPolicy for clarity and consistency with naming conventions. This change affects the factory and distributor to use the updated name. * applies spotless * nulls GPUs as it is not used
* Separate timeshift into an interface and add it to memorizing * Run spotless apply * Remove random from memorizing sched test * Record time on task termination * spotless apply