diff --git a/python/ray/autoscaler/_private/load_metrics.py b/python/ray/autoscaler/_private/load_metrics.py
index bf9dc564b..09ea11238 100644
--- a/python/ray/autoscaler/_private/load_metrics.py
+++ b/python/ray/autoscaler/_private/load_metrics.py
@@ -5,6 +5,7 @@ import time
 from typing import Dict, List
 
 import numpy as np
+import ray.ray_constants
 import ray._private.services as services
 from ray.autoscaler._private.constants import MEMORY_RESOURCE_UNIT_BYTES,\
     AUTOSCALER_MAX_RESOURCE_DEMAND_VECTOR_SIZE
@@ -212,8 +213,15 @@ class LoadMetrics:
                                  ) if self.static_resources_by_ip else {}
         usage_dict = {}
         for key in total_resources:
-            total = total_resources[key]
-            usage_dict[key] = (total - available_resources[key], total)
+            if key in ["memory", "object_store_memory"]:
+                total = total_resources[key] * \
+                    ray.ray_constants.MEMORY_RESOURCE_UNIT_BYTES
+                available = available_resources[key] * \
+                    ray.ray_constants.MEMORY_RESOURCE_UNIT_BYTES
+                usage_dict[key] = (total - available, total)
+            else:
+                total = total_resources[key]
+                usage_dict[key] = (total - available_resources[key], total)
 
         summarized_demand_vector = freq_of_dicts(
             self.get_resource_demand_vector(clip=False))
diff --git a/python/ray/autoscaler/_private/util.py b/python/ray/autoscaler/_private/util.py
index 39ebd5e79..788da5cc2 100644
--- a/python/ray/autoscaler/_private/util.py
+++ b/python/ray/autoscaler/_private/util.py
@@ -313,12 +313,12 @@ def format_pg(pg):
 
 def get_usage_report(lm_summary) -> str:
     usage_lines = []
-    for resource, (used, total) in lm_summary.usage.items():
+    for resource, (used, total) in sorted(lm_summary.usage.items()):
         if "node:" in resource:
             continue  # Skip the auto-added per-node "node:<ip>" resource.
         line = f" {used}/{total} {resource}"
         if resource in ["memory", "object_store_memory"]:
-            to_GiB = ray.ray_constants.MEMORY_RESOURCE_UNIT_BYTES / 2**30
+            to_GiB = 1 / 2**30
             used *= to_GiB
             total *= to_GiB
             line = f" {used:.2f}/{total:.3f} GiB {resource}"
diff --git a/python/ray/tests/test_resource_demand_scheduler.py b/python/ray/tests/test_resource_demand_scheduler.py
index 977c2f2b8..d753ffcab 100644
--- a/python/ray/tests/test_resource_demand_scheduler.py
+++ b/python/ray/tests/test_resource_demand_scheduler.py
@@ -8,6 +8,7 @@ import unittest
 import copy
 
 import ray
+import ray.ray_constants
 from ray.autoscaler._private.util import \
     rewrite_legacy_yaml_to_available_node_types, format_info_string, \
     format_info_string_no_node_types
@@ -1215,15 +1216,27 @@ class LoadMetricsTest(unittest.TestCase):
                 strategy=PlacementStrategy.PACK,
                 bundles=([Bundle(unit_resources={"GPU": 2})] * 2)),
         ]
-        lm.update("1.1.1.1", {"CPU": 64}, {"CPU": 2}, {})
+        lm.update(
+            "1.1.1.1",
+            {
+                "CPU": 64,
+                "memory": 20,  # 1000 MiB
+                "object_store_memory": 40  # 2000 MiB
+            },
+            {
+                "CPU": 2,
+                "memory": 10,  # 500 MiB
+                "object_store_memory": 20  # 1000 MiB
+            },
+            {})
         lm.update("1.1.1.2", {
             "CPU": 64,
             "GPU": 8,
-            "accelerator_type:V100": 1
+            "accelerator_type:V100": 1,
         }, {
             "CPU": 0,
             "GPU": 1,
-            "accelerator_type:V100": 1
+            "accelerator_type:V100": 1,
         }, {})
         lm.update("1.1.1.3", {
             "CPU": 64,
@@ -1257,6 +1270,9 @@ class LoadMetricsTest(unittest.TestCase):
 
         assert summary.usage["CPU"] == (190, 194)
         assert summary.usage["GPU"] == (15, 16)
+        assert summary.usage["memory"] == (500 * 2**20, 1000 * 2**20)
+        assert summary.usage["object_store_memory"] == \
+            (1000 * 2**20, 2000 * 2**20)
         assert summary.usage["accelerator_type:V100"][1] == 2, \
             "Not comparing the usage value due to floating point error."
 
@@ -1280,7 +1296,7 @@ class LoadMetricsTest(unittest.TestCase):
         # TODO (Alex): This set of nodes won't be very useful in practice
         # because the node:xxx.xxx.xxx.xxx resources means that no 2 nodes
         # should ever have the same set of resources.
-        assert len(summary.node_types) == 3
+        assert len(summary.node_types) == 3, summary.node_types
 
 
 class AutoscalingTest(unittest.TestCase):
@@ -2413,8 +2429,8 @@ def test_info_string():
             "CPU": (530, 544),
             "GPU": (2, 2),
             "AcceleratorType:V100": (0, 2),
-            "memory": (0, 1583.19),
-            "object_store_memory": (0, 471.02)
+            "memory": (2 * 2**30, 2**33),
+            "object_store_memory": (3.14 * 2**30, 2**34)
         },
         resource_demand=[({
             "CPU": 1
@@ -2457,11 +2473,11 @@ Resources
 --------------------------------------------------------
 
 Usage:
+ 0/2 AcceleratorType:V100
  530/544 CPU
  2/2 GPU
- 0/2 AcceleratorType:V100
- 0.00/77.304 GiB memory
- 0.00/22.999 GiB object_store_memory
+ 2.00/8.000 GiB memory
+ 3.14/16.000 GiB object_store_memory
 
 Demands:
  {'CPU': 1}: 150+ pending tasks/actors
@@ -2484,8 +2500,8 @@ def test_info_string_no_node_type():
             "CPU": (530, 544),
             "GPU": (2, 2),
             "AcceleratorType:V100": (0, 2),
-            "memory": (0, 1583.19),
-            "object_store_memory": (0, 471.02)
+            "memory": (2 * 2**30, 2**33),
+            "object_store_memory": (3.14 * 2**30, 2**34)
         },
         resource_demand=[({
             "CPU": 1
@@ -2512,11 +2528,11 @@ Node status
 Resources
 -----------------------------------------------------
 Usage:
+ 0/2 AcceleratorType:V100
  530/544 CPU
  2/2 GPU
- 0/2 AcceleratorType:V100
- 0.00/77.304 GiB memory
- 0.00/22.999 GiB object_store_memory
+ 2.00/8.000 GiB memory
+ 3.14/16.000 GiB object_store_memory
 
 Demands:
  {'CPU': 1}: 150+ pending tasks/actors